The diverse nature of dialects presents challenges for models trained on
specific linguistic patterns, rendering them susceptible to errors when
confronted with unseen or out-of-distribution (OOD) data. This study introduces
a novel margin-enhanced joint energy model (MEJEM) tailored specifically for
OOD detection in dialects. By integrating a generative model and the energy
margin loss, our approach aims to enhance the robustness of dialect
identification systems. Furthermore, we explore two OOD scores for OOD dialect
detection, and our findings conclusively demonstrate that the energy score
outperforms the softmax score. Leveraging Sharpness-Aware Minimization to
optimize the training process of the joint model, we enhance model
generalization by minimizing both loss and sharpness. Experiments conducted on
dialect identification tasks validate the efficacy of Energy-Based Models and
provide valuable insights into their performance.

本研究介绍了一种针对方言的新型边界增强联合能量模型（MEJEM），用于方言中的 OOD 检测。通过集成生成模型和能量边界损失，我们的方法旨在增强方言识别系统的鲁棒性。此外，我们探索了两种 OOD 得分的方言检测方法，研究结果明确表明能量得分优于 softmax 得分。利用锐度感知最小化优化联合模型的训练过程，通过最小化损失和锐度的方法增强模型的泛化性。在方言识别任务上进行的实验证实了基于能量的模型的有效性，并提供了有价值的性能见解。